Determining and compensating for temporal overhead in trace record generation and processing

ABSTRACT

A program is profiled with enablement of trace record generation during a first period of time and with disablement of trace record generation during a second period of time. The number of trace records output during the first period of time is determined, and a trace overhead calibration value is computed as an average time for writing the number of trace records output during the first period of time. The trace overhead calibration value may be stored for subsequent use in a profiling-related process in the data processing system. The trace overhead compensation value represents the amount of time required to generate a trace record, and the trace times retrieved from the trace records are adjusted to compensate for the amount of time required to generate those trace records.

CROSS-REFERENCE TO RELATED APPLICATIONS

The present invention is related to the following applications entitled“METHOD AND SYSTEM FOR COMPENSATING FOR INSTRUMENTATION OVERHEAD INTRACE DATA BY COMPUTING AVERAGE MINIMUM EVENT TIMES”, U.S. applicationSer. No. 09/393,088, filed on Sep. 9, 1999, now U.S. Pat. No. 6,349,406;“METHOD AND SYSTEM FOR COMPENSATING FOR OUTPUT OVERHEAD IN TRACE DATAUSING TRACE RECORD INFORMATION”, U.S. application Ser. No. 09/414,344,filed on Oct. 7, 1999; “METHOD AND SYSTEM FOR COMPENSATING FORINSTRUMENTATION OVERHEAD IN TRACE DATA BY DETECTING MINIMUM EVENTTIMES”, U.S. application Ser. No. 09/393,086, filed on Sep. 9, 1999;“METHOD AND SYSTEM FOR COMPENSATING FOR OUTPUT OVERHEAD IN TRACE DATAUSING INITIAL CALIBRATION INFORMATION”, U.S. application Ser. No.09/414,345, filed on Oct. 7, 1999; all of which are assigned to the sameassignee. The present application is also related to “A METHOD ANDAPPARATUS FOR STRUCTURED MEMORY ANALYSIS OF DATA PROCESSING SYSTEMS ANDAPPLICATIONS,” U.S. patent application Ser. No. 09/052,331, filed Mar.31, 1998, now U.S. Pat. No. 6,158,024, which is hereby incorporated byreference.

BACKGROUND OF THE INVENTION

1. Technical Field

The present invention relates to an improved data processing system and,in particular, to a method and apparatus for optimizing performance in adata processing system. Still more particularly, the present inventionprovides a method and apparatus for a software program development toolfor enhancing performance of a software program through softwareprofiling.

2. Description of Related Art

In analyzing and enhancing performance of a data processing system andthe applications executing within the data processing system, it ishelpful to know which software modules within a data processing systemare using system resources. Effective management and enhancement of dataprocessing systems requires knowing how and when various systemresources are being used. Performance tools are used to monitor andexamine a data processing system to determine resource consumption asvarious software applications are executing within the data processingsystem. For example, a performance tool may identify the most frequentlyexecuted modules and instructions in a data processing system, or mayidentify those modules which allocate the largest amount of memory orperform the most I/O requests. Hardware performance tools may be builtinto the system or added at a later point in time. Software performancetools also are useful in data processing systems, such as personalcomputer systems, which typically do not contain many, if any, built-inhardware performance tools.

One known software performance tool is a trace tool. A trace tool mayuse more than one technique to provide trace information that indicatesexecution flows for an executing program. One technique keeps track ofparticular sequences of instructions by logging certain events as theyoccur, so-called event-based profiling technique. For example, a tracetool may log every entry into, and every exit from, a module,subroutine, method, function, or system component. Alternately, a tracetool may log the requester and the amounts of memory allocated for eachmemory allocation request. Typically, a time-stamped record is producedfor each such event. Corresponding pairs of records similar toentry-exit records also are used to trace execution of arbitrary codesegments, starting and completing I/O or data transmission, and for manyother events of interest.

In order to improve performance of code generated by various families ofcomputers, it is often necessary to determine where time is being spentby the processor in executing code, such efforts being commonly known inthe computer processing arts as locating “hot spots.” Ideally, one wouldlike to isolate such hot spots at the instruction and/or source line ofcode level in order to focus attention on areas which might benefit mostfrom improvements to the code.

Another trace technique involves program sampling to identify certainlocations in programs in which the programs appear to spend largeamounts of time. This technique is based on the idea of interrupting theapplication or data processing system execution at regular intervals,so-called sample-based profiling. At each interruption, the programcounter of the currently executing thread, a process that is part of alarger process or program, is recorded. Typically, these tools capturevalues that are resolved against a load map and symbol table informationfor the data processing system at post-processing time, and a profile ofwhere the time is being spent is obtained from this analysis.

For example, isolating such hot spots down to the instruction levelpermits compiler writers to find significant areas of suboptimal codegeneration, at which they may focus their efforts to improve codegeneration efficiency. Another potential use of instruction level detailis to provide guidance to the designer of future systems. Such designersemploy profiling tools to find characteristic code sequences and/orsingle instructions that require optimization for the available softwarefor a given type of hardware.

There are often costs associated with measuring a system in that themeasurement itself perturbs the system. This effect is well understoodin the study of elementary particle physics and is known as theHeisenberg uncertainty principle. With software tracing, the costassociated with the tracing can severely affect the system beingprofiled. The effect can range from disruption of the cache and theinstruction pipeline to more mundane effects such as the overheadassociated with the tracing.

Although the writing of a single trace record may require a relativelyshort amount of time, the profiling process may cause the writing ofthousands of trace records. The total amount of time spent outputtingthe trace records may then grow to a significant percentage of the timethat may be attributed to particular routines. If such output overheadtime is not determined and the execution times of the routines are notcompensated for this overhead time, then the profile of the execution ofthe program may contain significant distortion as to the time spentwithin various routines. If the overhead time is significant, ascompared to the time spent within the routine being measured, asignificant distortion in the results would be realized if notcompensated.

Therefore, it would be particularly advantageous to provide a method andsystem for determining the amount of output overhead incurred during theprofiling of a program and for compensating for the overhead.

SUMMARY OF THE INVENTION

A method and system for compensating for trace overhead is provided byanalyzing and compensating for the temporal overhead associated withgenerating or outputting trace information to a trace buffer or a tracefile in the form of trace records. A trace record generally representsan occurrence of a profiling event attributable to a particular routinewithin a profiled program. A program is profiled with enablement oftrace record generation during a first period of time and withdisablement of trace record generation during a second period of time.The number of trace records output during the first period of time isdetermined, and a trace overhead calibration value is computed as anaverage time for writing the number of trace records output during thefirst period of time. The trace overhead calibration value may be storedfor subsequent use in a profiling-related process in the data processingsystem. The trace overhead compensation value represents the amount oftime required to generate a trace record, and the trace times retrievedfrom the trace records are adjusted to compensate for the amount of timerequired to generate those trace records.

BRIEF DESCRIPTION OF THE DRAWINGS

The novel features believed characteristic of the invention are setforth in the appended claims. The invention itself, however, as well asa preferred mode of use, further objectives and advantages thereof, willbest be understood by reference to the following detailed description ofan illustrative embodiment when read in conjunction with theaccompanying drawings, wherein:

FIG. 1 depicts a distributed data processing system in which the presentinvention may be implemented;

FIGS. 2A-2B are block diagrams depicting a data processing system inwhich the present invention may be implemented;

FIG. 3A is a block diagram depicting the relationship of softwarecomponents operating within a computer system that may implement thepresent invention;

FIG. 3B is a block diagram depicting a Java virtual machine inaccordance with a preferred embodiment of the present invention;

FIG. 4 is a block diagram depicting components used to profile processesin a data processing system;

FIG. 5 is an illustration depicting various phases in profiling theactive processes in an operating system;

FIG. 6 is a flowchart depicting a process used by a trace program forgenerating trace records from processes executing on a data processingsystem;

FIG. 7 is a flowchart depicting a process used in a system interrupthandler trace hook;

FIG. 8 is a diagram depicting the call stack containing stack frames;

FIG. 9 is an illustration depicting a call stack;

FIG. 10A is a diagram depicting a program execution sequence along withthe state of the call stack at each function entry/exitpoint;

FIG. 10B is a diagram depicting a particular timer based sampling of theexecution flow depicted in FIG. 10A;

FIGS. 10C-10D are time charts providing an example of the types of timefor which the profiling tool accounts;

FIG. 11A is a diagram depicting a tree structure generated from samplinga call stack;

FIG. 11B is a diagram depicting an event tree which reflects call stacksobserved during system execution;

FIG. 12 is a table depicting a call stack tree;

FIG. 13 is a flow chart depicting a method for building a call stacktree using a trace text file as input;

FIG. 14 is a flow chart depicting a method for building a call stacktree dynamically as tracing is taking place during system execution;

FIG. 15 is a diagram depicting a structured profile obtained using theprocesses of the present invention;

FIG. 16 is a diagram depicting a record generated using the processes ofpresent invention;

FIG. 17 is a diagram depicting another type of report that may beproduced to show the calling structure between routines shown in FIG.12;

FIG. 18 is a table depicting a report generated from a trace filecontaining both event-based profiling information (method entry/exits)and sample-based profiling information (stack unwinds);

FIGS. 19A-19B are tables depicting major codes and minor codes that maybe employed to instrument modules for profiling;

FIG. 20A is a flowchart depicting a summary of the manner in whichelapsed time is attributed to various routines in an execution flow;

FIG. 20B is a timeline depicting demarcated time points with which theprocess in FIG. 20A is concerned;

FIG. 21 is an illustration depicting a trace record format that containsinformation from which compensation information may be obtained forcompensating for trace record generation overhead;

FIG. 22 is a flowchart depicting a process for profiling the executionof a program with the determination of calibration values to be used tocompensate for the instrumentation overhead introduced by the profilingprocesses;

FIG. 23 is a flowchart depicting a process for determining an outputoverhead calibration value by comparing a calibration run with traceoutput enabled with a calibration run with trace output disabled;

FIG. 24A is a flowchart depicting a process for compensating for traceoverhead using an trace overhead compensation value;

FIG. 24B is a timeline depicting the time points with which the processwithin FIG. 24A is concerned;

FIG. 25A is a flowchart depicting a second process for compensating fortrace overhead using an trace overhead compensation value;

FIG. 25B is a timeline depicting the time points with which the processwithin FIG. 25A is concerned;

DETAILED DESCRIPTION OF THE PREFERRED EMBODIMENT

With reference now to the figures, and in particular with reference toFIG. 1, a pictorial representation of a distributed data processingsystem in which the present invention may be implemented is depicted.

Distributed data processing system 100 is a network of computers inwhich the present invention may be implemented. Distributed dataprocessing system 100 contains a network 102, which is the medium usedto provide communications links between various devices and computersconnected together within distributed data processing system 100.Network 102 may include permanent connections, such as wire or fiberoptic cables, or temporary connections made through telephoneconnections.

In the depicted example, a server 104 is connected to network 102 alongwith storage unit 106. In addition, clients 108, 110, and 112 also areconnected to a network 102. These clients 108, 110, and 112 may be, forexample, personal computers or network computers. For purposes of thisapplication, a network computer is any computer, coupled to a network,which receives a program or other application from another computercoupled to the network. In the depicted example, server 104 providesdata, such as boot files, operating system images, and applications toclients 108-112. Clients 108, 110, and 112 are clients to server 104.Distributed data processing system 100 may include additional servers,clients, and other devices not shown. In the depicted example,distributed data processing system 100 is the Internet with network 102representing a worldwide collection of networks and gateways that usethe TCP/IP suite of protocols to communicate with one another. At theheart of the Internet is a backbone of high-speed data communicationlines between major nodes or host computers, consisting of thousands ofcommercial, government, educational, and other computer systems, thatroute data and messages. Of course, distributed data processing system100 also may be implemented as a number of different types of networks,such as, for example, an Intranet or a local area network.

FIG. 1 is intended as an example, and not as an architectural limitationfor the processes of the present invention.

With reference now to FIG. 2A, a block diagram of a data processingsystem which may be implemented as a server, such as server 104 in FIG.1, is depicted in accordance to the present invention. Data processingsystem 200 may be a symmetric multiprocessor (SMP) system including aplurality of processors 202 and 204 connected to system bus 206.Alternatively, a single processor system may be employed. Also connectedto system bus 206 is memory controller/cache 208, which provides aninterface to local memory 209. I/O Bus Bridge 210 is connected to systembus 206 and provides an interface to I/O bus 212. Memorycontroller/cache 208 and I/O Bus Bridge 210 may be integrated asdepicted.

Peripheral component interconnect (PCI) bus bridge 214 connected to I/Obus 212 provides an interface to PCI local bus 216. A modem 218 may beconnected to PCI local bus 216. Typical PCI bus implementations willsupport four PCI expansion slots or add-in connectors. Communicationslinks to network computers 108-112 in FIG. 1 may be provided throughmodem 218 and network adapter 220 connected to PCI local bus 216 throughadd-in boards.

Additional PCI bus bridges 222 and 224 provide interfaces for additionalPCI buses 226 and 228, from which additional modems or network adaptersmay be supported. In this manner, server 200 allows connections tomultiple network computers. A memory mapped graphics adapter 230 andhard disk 232 may also be connected to I/O bus 212 as depicted, eitherdirectly or indirectly.

Those of ordinary skill in the art will appreciate that the hardwaredepicted in FIG. 2A may vary. For example, other peripheral devices,such as optical disk drive and the like also may be used in addition orin place of the hardware depicted. The depicted example is not meant toimply architectural limitations with respect to the present invention.

The data processing system depicted in FIG. 2A may be, for example, anIBM RISC/System 6000 system, a product of International BusinessMachines Corporation in Armonk, N.Y., running the Advanced InteractiveExecutive (AIX) operating system.

With reference now to FIG. 2B, a block diagram of a data processingsystem in which the present invention may be implemented is illustrated.Data processing system 250 is an example of a client computer. Dataprocessing system 250 employs a peripheral component interconnect (PCI)local bus architecture. Although the depicted example employs a PCI bus,other bus architectures such as Micro Channel and ISA may be used.Processor 252 and main memory 254 are connected to PCI local bus 256through PCI Bridge 258. PCI Bridge 258 also may include an integratedmemory controller and cache memory for processor 252. Additionalconnections to PCI local bus 256 may be made through direct componentinterconnection or through add-in boards. In the depicted example, localarea network (LAN) adapter 260, SCSI host bus adapter 262, and expansionbus interface 264 are connected to PCI local bus 256 by direct componentconnection. In contrast, audio adapter 266, graphics adapter 268, andaudio/video adapter (A/V) 269 are connected to PCI local bus 266 byadd-in boards inserted into expansion slots. Expansion bus interface 264provides a connection for a keyboard and mouse adapter 270, modem 272,and additional memory 274. SCSI host bus adapter 262 provides aconnection for hard disk drive 276, tape drive 278, and CD-ROM 280 inthe depicted example. Typical PCI local bus implementations will supportthree or four PCI expansion slots or add-in connectors.

An operating system runs on processor 252 and is used to coordinate andprovide control of various components within data processing system 250in FIG. 2B. The operating system may be a commercially availableoperating system such as JavaOS For Business™ or OS/2™ which areavailable from International Business Machines Corporation™. JavaOS isloaded from a server on a network to a network client and supports Javaprograms and applets. A couple of characteristics of JavaOS that arefavorable for performing traces with stack unwinds, as described below,are that JavaOS does not support paging or virtual memory. An objectoriented programming system such as Java may run in conjunction with theoperating system and may provide calls to the operating system from Javaprograms or applications executing on data processing system 250.Instructions for the operating system, the object-oriented operatingsystem, and applications or programs are located on storage devices,such as hard disk drive 276 and may be loaded into main memory 254 forexecution by processor 252. Hard disk drives are often absent and memoryis constrained when data processing system 250 is used as a networkclient.

Those of ordinary skill in the art will appreciate that the hardware inFIG. 2B may vary depending on the implementation. For example, otherperipheral devices, such as optical disk drives and the like may be usedin addition to or in place of the hardware depicted in FIG. 2B. Thedepicted example is not meant to imply architectural limitations withrespect to the present invention. For example, the processes of thepresent invention may be applied to a multiprocessor data processingsystem.

The present invention provides a process and system for profilingsoftware applications. Although the present invention may operate on avariety of computer platforms and operating systems, it may also operatewithin a Java runtime environment. Hence, the present invention mayoperate in conjunction with a Java virtual machine (JVM) yet within theboundaries of a JVM as defined by Java standard specifications. In orderto provide a context for the present invention, portions of theoperation of a JVM according to Java specifications are hereindescribed.

With reference now to FIG. 3A, a block diagram illustrates therelationship of software components operating within a computer systemthat may implement the present invention. Java-based system 300 containsplatform specific operating system 302 that provides hardware and systemsupport to software executing on a specific hardware platform. JVM 304is one software application that may execute in conjunction with theoperating system. JVM 304 provides a Java run-time environment with theability to execute Java application or applet 306, which is a program,servlet, or software component written in the Java programming language.The computer system in which JVM 304 operates may be similar to dataprocessing system 200 or computer 100 described above. However, JVM 304may be implemented in dedicated hardware on a so-called Java chip,Java-on-silicon, or Java processor with an embedded picoJava core.

At the center of a Java run-time environment is the JVM, which supportsall aspects of Java's environment, including its architecture, securityfeatures, mobility across networks, and platform independence.

The JVM is a virtual computer, i.e. a computer that is specifiedabstractly. The specification defines certain features that every JVMmust implement, with some range of design choices that may depend uponthe platform on which the JVM is designed to execute. For example, allJVMs must execute Java bytecodes and may use a range of techniques toexecute the instructions represented by the bytecodes. A JVM may beimplemented completely in software or somewhat in hardware. Thisflexibility allows different JVMs to be designed for mainframe computersand PDAs.

The JVM is the name of a virtual computer component that actuallyexecutes Java programs. Java programs are not run directly by thecentral processor but instead by the JVM, which is itself a piece ofsoftware running on the processor. The JVM allows Java programs to beexecuted on a different platform as opposed to only the one platform forwhich the code was compiled. Java programs are compiled for the JVM. Inthis manner, Java is able to support applications for many types of dataprocessing systems, which may contain a variety of central processingunits and operating systems architectures. To enable a Java applicationto execute on different types of data processing systems, a compilertypically generates an architecture-neutral file format—the compiledcode is executable on many processors, given the presence of the Javarun-time system. The Java compiler generates bytecode instructions thatare nonspecific to a particular computer architecture. A bytecode is amachine independent code generated by the Java compiler and executed bya Java interpreter. A Java interpreter is part of the JVM thatalternately decodes and interprets a bytecode or bytecodes. Thesebytecode instructions are designed to be easy to interpret on anycomputer and easily translated on the fly into native machine code. Bytecodes are may be translated into native code by a just-in-time compileror JIT.

A JVM must load class files and execute the bytecodes within them. TheJVM contains a class loader, which loads class files from an applicationand the class files from the Java application programming interfaces(APIS) which are needed by the application. The execution engine thatexecutes the bytecodes may vary across platforms and implementations.

One type of software-based execution engine is a just-in-time compiler.With this type of execution, the bytecodes of a method are compiled tonative machine code upon successful fulfillment of some type of criteriafor jitting a method. The native machine code for the method is thencached and reused upon the next invocation of the method. The executionengine may also be implemented in hardware and embedded on a chip sothat the Java bytecodes are executed natively. JVMs usually interpretbytecodes, but JVMs may also use other techniques, such as just-in-timecompiling, to execute bytecodes.

Interpreting code provides an additional benefit. Rather thaninstrumenting the Java source code, the interpreter may be instrumented.Trace data may be generated via selected events and timers through theinstrumented interpreter without modifying the source code. Profileinstrumentation is discussed in more detail further below.

When an application is executed on a JVM that is implemented in softwareon a platform-specific operating system, a Java application may interactwith the host operating system by invoking native methods. A Java methodis written in the Java language, compiled to bytecodes, and stored inclass files. A native method is written in some other language andcompiled to the native machine code of a particular processor. Nativemethods are stored in a dynamically linked library whose exact form isplatform specific.

With reference now to FIG. 3B, a block diagram of a JVM is depicted inaccordance with a preferred embodiment of the present invention. JVM 350includes a class loader subsystem 352, which is a mechanism for loadingtypes, such as classes and interfaces, given fully qualified names. JVM350 also contains runtime data areas 354, execution engine 356, nativemethod interface 358, and memory management 374. Execution engine 356 isa mechanism for executing instructions contained in the methods ofclasses loaded by class loader subsystem 352. Execution engine 356 maybe, for example, Java interpreter 362 or just-in-time compiler 360.Native method interface 358 allows access to resources in the underlyingoperating system. Native method interface 358 may be, for example, aJava native interface.

Runtime data areas 354 contain native method stacks 364, Java stacks366, PC registers 368, method area 370, and heap 372. These differentdata areas represent the organization of memory needed by JVM 350 toexecute a program.

Java stacks 366 are used to store the state of Java method invocations.When a new thread is launched, the JVM creates a new Java stack for thethread. The JVM performs only two operations directly on Java stacks: itpushes and pops frames. A thread's Java stack stores the state of Javamethod invocations for the thread. The state of a Java method invocationincludes its local variables, the parameters with which it was invoked,its return value, if any, and intermediate calculations. Java stacks arecomposed of stack frames. A stack frame contains the state of a singleJava method invocation. When a thread invokes a method, the JVM pushes anew frame onto the Java stack of the thread. When the method completes,the JVM pops the frame for that method and discards it. The JVM does nothave any registers for holding intermediate values; any Java instructionthat requires or produces an intermediate value uses the stack forholding the intermediate values. In this manner, the Java instructionset is well-defined for a variety of platform architectures.

PC registers 368 are used to indicate the next instruction to beexecuted. Each instantiated thread gets its own pc register (programcounter) and Java stack. If the thread is executing a JVM method, thevalue of the pc register indicates the next instruction to execute. Ifthe thread is executing a native method, then the contents of the pcregister are undefined.

Native method stacks 364 store the state of invocations of nativemethods. The state of native method invocations is stored in animplementation-dependent way in native method stacks, registers, orother implementation-dependent memory areas. In some JVMimplementations, native method stacks 364 and Java stacks 366 arecombined.

Method area 370 contains class data while heap 372 contains allinstantiated objects. The JVM specification strictly defines data typesand operations. Most JVMs choose to have one method area and one heap,each of which are shared by all threads running inside the JVM. When theJVM loads a class file, it parses information about a type from thebinary data contained in the class file. It places this type informationinto the method area. Each time a class instance or array is created,the memory for the new object is allocated from heap 372. JVM 350includes an instruction that allocates memory space within the memoryfor heap 372 but includes no instruction for freeing that space withinthe memory. Memory management 374 in the depicted example manages memoryspace within the memory allocated to heap 370. Memory management 374 mayinclude a garbage collector which automatically reclaims memory used byobjects that are no longer referenced. Additionally, a garbage collectoralso may move objects to reduce heap fragmentation.

The processes within the following figures provide an overallperspective of the many processes employed within the present invention:processes that generate event-based profiling information in the form ofspecific types of records in a trace file; processes that generatesample-based profiling information in the form of specific types ofrecords in a trace file; processes that read the trace records togenerate more useful information to be placed into profile reports; andprocesses that generate the profile reports for the user of theprofiling utility.

With reference now to FIG. 4, a block diagram depicts components used toprofile processes in a data processing system. A trace program 400 isused to profile processes 402. Trace program 400 may be used to recorddata upon the execution of a hook, which is a specialized piece of codeat a specific location in a routine or program in which other routinesmay be connected. Trace hooks are typically inserted for the purpose ofdebugging, performance analysis, or enhancing functionality. These tracehooks are employed to send trace data to trace program 400, which storesthe trace data in buffer 404. The trace data in buffer 404 may be storedin a file for post-processing. With Java operating systems, the presentinvention employs trace hooks that aid in identifying methods that maybe used in processes 402. In addition, since classes may be loaded andunloaded, these changes may also be identified using trace data. This isespecially relevant with “network client” data processing systems, suchas those that may operate under JavaOS, since classes and jitted methodsmay be loaded and unloaded more frequently due to the constrained memoryand role as a network client.

With reference now to FIG. 5, a diagram depicts various phases inprofiling the processes active in an operating system. Subject to memoryconstraints, the generated trace output may be as long and as detailedas the analyst requires for the purpose of profiling a particularprogram.

An initialization phase 500 is used to capture the state of the clientmachine at the time tracing is initiated. This trace initialization dataincludes trace records that identify all existing threads, all loadedclasses, and all methods for the loaded classes. Records from trace datacaptured from hooks are written to indicate thread switches, interrupts,and loading and unloading of classes and jitted methods. Any class whichis loaded has trace records that indicate the name of the class and itsmethods. In the depicted example, four byte IDs are used as identifiersfor threads, classes, and methods. These IDs are associated with namesoutput in the records. A record is written to indicate when all of thestart up information has been written.

Next, during the profiling phase 502, trace records are written to atrace buffer or file. Trace records may originate from two types ofprofiling actions—event-based profiling and sample-based profiling. Inthe present invention, the trace file may have a combination ofevent-based records, such as those that may originate from a trace hookexecuted in response to a particular type of event, e.g., a method entryor method exit, and sample-based records, such as those that mayoriginate from a stack walking function executed in response to a timerinterrupt, e.g., a stack unwind record, also called a call stack record.

For example, the following process may occur during the profiling phaseif the user of the profiling utility has requested sample-basedprofiling information. Each time a particular type of timer interruptoccurs, a trace record is written, which indicates the system programcounter. This system program counter may be used to identify the routinethat is interrupted. In the depicted example, a timer interrupt is usedto initiate gathering of trace data of course, other types of interruptsmay be used other than timer interrupts. Interrupts based on aprogrammed performance monitor event or other types of periodic eventsmay be employed.

In the post-processing phase 504, the data collected in the buffer issent to a file for post-processing. In one configuration, the file maybe sent to a server, which determines the profile for the processes onthe client machine. Of course, depending on available resources, thepost-processing also may be performed on the client machine. Inpost-processing phase 504, B-trees and/or hash tables may be employed tomaintain names associated the records in the trace file to be processed.A hash table employs hashing to convert an identifier or a key,meaningful to a user, into a value for the location of the correspondingdata in the table. While processing trace records, the B-trees and/orhash tables are updated to reflect the current state of the clientmachine, including newly loaded jitted code or unloaded code. Also, inthe post-processing phase 504, each trace record is processed in aserial manner. As soon as the indicator is encountered that all of thestartup information has been processed, event-based trace records fromtrace hooks and sample-based trace records from timer interrupts arethen processed. Timer interrupt information from the timer interruptrecords are resolved with existing hash tables. In addition, thisinformation identifies the thread and function being executed. The datais stored in hash tables with a count identifying the number of timertick occurrences associated with each way of looking at the data. Afterall of the trace records are processed, the information is formatted foroutput in the form of a report.

Alternatively, trace information may be processed on-the-fly so thattrace data structures are maintained during the profiling phase. Inother words, while a profiling function, such as a timer interrupt, isexecuting, rather than (or in addition to) writing trace records to abuffer or file, the trace record information is processed to constructand maintain any appropriate data structures.

For example, during the processing of a timer interrupt during theprofiling phase, a determination could be made as to whether the codebeing interrupted is being interpreted by the Java interpreter. If thecode being interrupted is interpreted, the method ID of the method beinginterpreted may be placed in the trace record. In addition, the name ofthe method may be obtained and placed in the appropriate B-tree. Oncethe profiling phase has completed, the data structures may contain allthe information necessary for generating a profile report without theneed for post-processing of the trace file.

With reference now to FIG. 6, a flowchart depicts a process used by atrace program for generating trace records from processes executing on adata processing system. FIG. 6 provides further detail concerning thegeneration of trace records that were not described with respect to FIG.5.

Trace records may be produced by the execution of small pieces of codecalled “hooks”. Hooks may be inserted in various ways into the codeexecuted by processes, including statically (source code) anddynamically (through modification of a loaded executable). This processis employed after trace hooks have already been inserted into theprocess or processes of interest. The process begins by allocating abuffer (step 600), such as buffer 404 in FIG. 4. Next, in the depictedexample, trace hooks are turned on (step 602), and tracing of theprocesses on the system begins (step 604). Trace data is received fromthe processes of interest (step 606). This type of tracing may beperformed during phases 500 and/or 502. This trace data is stored astrace records in the buffer (step 608). A determination is made as towhether tracing has finished (step 610). Tracing finishes when the tracebuffer has been filled or the user stops tracing via a command andrequests that the buffer contents be sent to file. If tracing has notfinished, the process returns to step 606 as described above.

Otherwise,.when tracing is finished, the buffer contents are sent to afile for post-processing (step 612). A report is then generated inpost-processing (step 614) with the process terminating thereafter.

Although the depicted example uses post-processing to analyze the tracerecords, the processes of the present invention may be used to processtrace information in real-time depending on the implementation.

With reference now to FIG. 7, a flowchart depicts a process that may beused during an interrupt handler trace hook.

The process begins by obtaining a program counter (step 700). Typically,the program counter is available in one of the saved program stackareas. Thereafter, a determination is made as to whether the code beinginterrupted is interpreted code (step 702). This determination may bemade by determining whether the program counter is within an addressrange for the interpreter used to interpret bytecodes. If the code beinginterrupted is interpreted, a method block address is obtained for thecode being interpreted. A trace record is then written (step 706). Thetrace record is written by sending the trace information to a traceprogram, such as trace program 400, which generates trace records forpost-processing in the depicted example. This trace record is referredto as an interrupt record, or an interrupt hook.

This type of trace may be performed during phase 502. Alternatively, asimilar process, i.e. determining whether code that was interrupted isinterpreted code, may occur during post-processing of a trace file.

In addition to event-based profiling, a set of processes may be employedto obtain sample-based profiling information. As applications execute,the applications may be periodically interrupted in order to obtaininformation about the current runtime environment. This information maybe written to a buffer or file for post-processing, or the informationmay be processed on-the-fly into data structures representing an ongoinghistory of the runtime environment. FIGS. 8 and 9 describe sample-basedprofiling in more detail.

A sample-based profiler obtains information from the stack of aninterrupted thread. The thread is interrupted by a timer interruptpresently available in many operating systems. The user of the tracefacility selects either the program counter option or the stack unwindoption, which may be accomplished by enabling one major code or anothermajor code, as described further below. This timer interrupt is employedto sample information from a call stack. By walking back up the callstack, a complete call stack can be obtained for analysis. A “stackwalk” may also be described as a “stack unwind”, and the process of“walking the stack” may also be described as “unwinding the stack.” Eachof these terms illustrates a different metaphor for the process. Theprocess can be described as “walking” as the process must obtain andprocess the stack frames step-by-step. The process may also be describedas “unwinding” as the process must obtain and process the stack framesthat point to one another, and these pointers and their information mustbe “unwound” through many pointer dereferences.

The stack unwind follows the sequence of functions/method calls at thetime of the interrupt. A call stack is an ordered list of routines plusoffsets within routines (i.e. modules, functions, methods, etc.) thathave been entered during execution of a program. For example, if routineA calls routine B, and then routine B calls routine C, while theprocessor is executing instructions in routine C, the call stack is ABC.When control returns from routine C back to routine B, the call stack isAB. For more compact presentation and ease of interpretation within agenerated report, the names of the routines are presented without anyinformation about offsets. Offsets could be used for more detailedanalysis of the execution of a program, however, offsets are notconsidered further herein.

Thus, during timer interrupt processing or at post-processing, thegenerated sample-based profile information reflects a sampling of callstacks, not just leaves of the possible call stacks, as in some programcounter sampling techniques. A leaf is a node at the end of a branch,i.e. a node that has no descendants. A descendant is a child of a parentnode, and a leaf is a node that has no children.

With reference now FIG. 8, a diagram depicts the call stack containingstack frames. A “stack” is a region of reserved memory in which aprogram or programs store status data, such as procedure and functioncall addresses, passed parameters, and sometimes local variables. A“stack frame” is a portion of a thread's stack that represents localstorage (arguments, return addresses, return values, and localvariables) for a single function invocation. Every active thread ofexecution has a portion of system memory allocated for its stack space.A thread's stack consists of sequences of stack frames. The set offrames on a thread's stack represent the state of execution of thatthread at any time. Since stack frames are typically interlinked (e.g.,each stack frame points to the previous stack frame), it is oftenpossible to trace back up the sequence of stack frames and develop the“call stack”. A call stack represents all not-yet-completed functioncalls—in other words, it reflects the function invocation sequence atany point in time.

Call stack 800 includes information identifying the routine that iscurrently running, the routine that invoked it, and so on all the way upto the main program. Call stack 800 includes a number of stack frames802, 804, 806, and 808. In the depicted example, stack frame 802 is atthe top of call stack 800, while stack frame 808 is located at thebottom of call stack 800. The top of the call stack is also referred toas the “root”. The timer interrupt (found in most operating systems) ismodified to obtain the program counter value (pcv) of the interruptedthread, together with the pointer to the currently active stack framefor that thread. In the Intel architecture, this is typicallyrepresented by the contents of registers: EIP (program counter) and EBP(pointer to stack frame). By accessing the currently active stack frame,it is possible to take advantage of the (typical) stack frame linkageconvention in order to chain all of the frames together. Part of thestandard linkage convention also dictates that the function returnaddress be placed just above the invoked-function's stack frame; thiscan be used to ascertain the address for the invoked function. Whilethis discussion employs an Intel-based architecture, this example is nota restriction. Most architectures employ linkage conventions that can besimilarly navigated by a modified profiling interrupt handler.

When a timer interrupt occurs, the first parameter acquired is theprogram counter value. The next value is the pointer to the top of thecurrent stack frame for the interrupted thread. In the depicted example,this value would point to EBP 808 a in stack frame 808. In turn, EBP 808points to EBP 806 a in stack frame 806, which in turn points to EBP 804a in stack frame 804. In turn, this EBP points to EBP 802 a in stackframe 802. Within stack frames 802-808 are EIPs 802 b-808 b, whichidentify the calling routine's return address. The routines may beidentified from these addresses. Thus, routines are defined bycollecting all of the return addresses by walking up or backwardsthrough the stack.

With reference now to the FIG. 9, an illustration of a call stack isdepicted. A call stack, such as call stack 900 is obtained by walkingthe call stack. A call stack is obtained each time a periodic event,such as, for example, a timer interrupt occurs. These call stacks may bestored as call stack unwind trace records within the trace file forpost-processing or may be processed on-the-fly while the programcontinues to execute.

In the depicted example, call stack 900 contains a pid 902, which is theprocess identifier, and a tid 904, which is the thread identifier. Callstack 900 also contains addresses addr1 906, addr2 908 . . . addrN 910.In this example, addr1 906 represents the value of the program counterat the time of the interrupt. This address occurs somewhere within thescope of the interrupted function. addr2 908 represents an addresswithin the process that called the function that was interrupted. ForIntel-processor-based data processing systems, it represents the returnaddress for that call; decrementing that value by 4 results in theaddress of the actual call, also known as the call-site. Thiscorresponds with EIP 808 b in FIG. 8; addrN 910 is the top of the callstack (EIP 802 b). The call stack that would be returned if the timerinterrupt interrupted the thread whose call stack state is depicted inFIG. 8 would consist of: a pid, which is the process id of theinterrupted thread; a tid, which is the thread id for the interruptedthread; a pcv, which is a program counter value (not shown on FIG. 8)for the interrupted thread; EIP 808 b; EIP 806 b; EIP 804 b; and EIP 802b. In terms of FIG. 9, pcv=addr1, EIP 808 b=addr2, EIP 806 b=addr3, EIP804 b=addr4, EIP 802 b=addr5.

With reference now to FIG. 10A, a diagram of a program executionsequence along with the state of the call stack at each functionentry/exit point is provided. The illustration shows entries and exitsoccurring at regular time intervals, but this is only a simplificationfor the illustration. If each function (A, B, C, and X in the figure)were instrumented with entry/exit event hooks, then complete accountingof the time spent within and below each function would be readilyobtained. Note in FIG. 10A that at time 0, the executing thread is inroutine C. The call stack at time 0 is C. At time 1, routine C callsroutine A, and the call stack becomes CA and so on. It should be notedthat the call stack in FIG. 10A is a reconstructed call stack that isgenerated by processing the event-based trace records in a trace file tofollow such events as method entries and method exits.

The accounting technique and data structure are described in more detailfurther below. Unfortunately, this type of instrumentation can beexpensive, can introduce bias, and in some cases, can be hard to apply.Sample-based profiling, by sampling the program's call stack, helps toalleviate the performance bias (and other complications) that entry/exithooks produce.

Consider FIG. 10B, in which the same program is executed but is beingsampled on a regular basis (in the example, the interrupt occurs at afrequency equivalent to two timestamp values). Each sample includes asnapshot of the interrupted thread's call stack. Not all call stackcombinations are seen with this technique (note that routine X does notshow up at all in the set of call stack samples in FIG. 10B). This is anacceptable limitation of sampling. The idea is that with an appropriatesampling rate (e.g., 30-1000 times per second), the call stacks in whichmost of the time is spent will be identified. Although some call stacksare omitted, it is a minor-issue provided these call stacks arecombinations for which little time is consumed.

In the event-based traces, there is a fundamental assumption that thetraces contain information about routine entries and matching routineexits. Often, entry-exit pairs are nested in the traces because routinescall other routines. Time spent (or memory consumed) between entry intoa routine and exit from the same routine is attributed to that routine,but a user of a profiling tool may want to distinguish between timespent directly in a routine and time spent in other routines that itcalls.

FIG. 10C shows an example of the manner in which time may be expended bytwo routines: a program's “main” calls routine A at time “t” equal tozero; routine A computes for 1 ms and then calls routine B; routine Bcomputes for 8 ms and then returns to routine A; routine A computes for1 ms and then returns to “main”. From the point of view of “main”,routine A took 10 ms to execute, but most of that time was spentexecuting instructions in routine B and was not spent executinginstructions within routine A. This is a useful piece of information fora person attempting to optimize the example program. In addition, ifroutine B is called from many places in the program, it might be usefulto know how much of the time spent in routine B was on behalf of (orwhen called by) routine A and how much of the time was on behalf ofother routines.

A fundamental concept in the output provided by the methods describedherein is the call stack. The call stack consists of the routine that iscurrently running, the routine that invoked it, and so on all the way upto main. A profiler may add a higher, thread level with the pid/tid (theprocess IDs and thread IDs). In any case, an attempt is made to followthe trace event records, such as method entries and exits, as shown inFIG. 10A, to reconstruct the structure of the call stack frames whilethe program was executing at various times during the trace.

The post-processing of a trace file may result in a report consisting ofthree kinds of time spent in a routine, such as routine A: (1) basetime—the time spent executing code in routine A itself; (2) cumulativetime (or “cum time” for short)—the:time spent executing in routine Aplus all the time spent executing every routine that routine A calls(and all the routines they call, etc.); and (3) wall-clock time orelapsed time. This type of timing information may be obtained fromevent-based trace records as these records have timestamp informationfor each record.

A routine's cum time is the sum of all the time spent executing theroutine plus the time spent executing any other routine while thatroutine is below it on the call stack. In the example above in FIG. 10C,routine A's base time is 2 ms, and its cum time is 10 ms. Routine B'sbase time is 8 ms, and its cum time is also 8=ms because it does notcall any other routines. It should be noted that cum time may not begenerated if a call stack tree is being generated on-the-fly—cum timemay only be computed after the fact during the post-processing phase ofa profile utility.

For wall-clock or elapsed time, if while routine B was running, thesystem fielded an interrupt or suspended this thread to run anotherthread, or if routine B blocked waiting on a lock or I/O, then routine Band all the entries above routine B on the call stack accumulate elapsedtime but not base or cum time. Base and cum time are unaffected byinterrupts, dispatching, or blocking. Base time only increases while aroutine is running, and cum time only increases while the routine or aroutine below it on the call stack is running.

In the example in FIG. 10C, routine A's elapsed time is the same as itscum time—10 ms. Changing the example slightly, suppose there was a 1 msinterrupt in the middle of B, as shown in FIG. 10D. Routine A's base andcum time are unchanged at 2 ms and 10 ms, but its elapsed time is now 11ms.

Although base time, cum time and elapsed time were defined in terms ofprocessor time spent in routines, sample based profiling is useful forattributing consumption of almost any system resource to a set ofroutines, as described in more detail below with respect to FIG. 11B.Referring to FIG. 10C again, if routine A initiated two disk I/O's, andthen routine B initiated three more I/O's when called by routine A,routine A's “base I/O's” are two and routine A's “cum I/O's” are five.“Elapsed I/O's” would be all I/O's, including those by other threads andprocesses, that occurred between entry to routine A and exit fromroutine A. More general definitions for the accounting concepts duringprofiling would be the following: base—the amount of the tracked systemresource consumed directly by this routine; cum—the amount of thetracked system resource consumed by this routine and all routines belowit on the call stack; elapsed—the total amount of the tracked systemresource consumed (by any routine) between entry to this routine andexit from the routine.

As noted above, FIGS. 10A-10D describe the process by which areconstructed call stack may be generated by processing the event-basedtrace records in a trace file by following such events as method entriesand method exits. Hence, although FIGS. 11A-14 describe call stack treesthat may be applicable to processing sample-based trace records, thedescription below for generating or reconstructing call stacks and callstack trees in FIGS. 11A-14 is mainly directed to the processing ofevent-based trace records.

With reference now to FIG. 11A, a diagram depicts a tree structuregenerated from trace data. This figure illustrates a call stack tree1100 in which each node in tree structure 1100 represents a functionentry point.

Additionally, in each node in tree structure 1100, a number ofstatistics are recorded. In the depicted example, each node, nodes1102-1108, contains an address (addr), a base time (BASE), cumulativetime (CUM) and parent and children pointers. As noted above, this typeof timing information may be obtained from event-based trace records asthese records have timestamp information for each record. The addressrepresents a function entry point. The base time represents the amountof time consumed directly by this thread executing this function. Thecumulative time is the amount of time consumed by this thread executingthis function and all functions below it on the call stack. In thedepicted example, pointers are included for each node. One pointer is aparent pointer, a pointer to the node's parent. Each node also containsa pointer to each child of the node.

Those of ordinary skill in the art will appreciate that tree structure1100 may be implemented in a variety of ways and that many differenttypes of statistics may be maintained at the nodes other than those inthe depicted example.

The call stack is developed from looking back at all return addresses.These return addresses will resolve within the bodies of thosefunctions. This information allows for accounting discrimination betweendistinct invocations of the same function. In other words, if function Xhas 2 distinct calls to function A, the time associated with those callscan: be accounted for separately. However, most reports would not makethis distinction.

With reference now to FIG. 11B, a call stack tree which reflects callstacks observed during a specific example of system execution will nowbe described. At each node in the tree, several statistics are recorded.In the example shown in FIG. 11B, the statistics are time-basedstatistics. The particular statistics shown include the number ofdistinct times the call stack is produced, the sum of the time spent inthe call stack, the total time spent in the call stack plus the time inthose call stacks invoked from this call stack (referred to ascumulative time), and the number of instances of this routine above thisinstance (indicating depth of recursion).

For example, at node 1152 in FIG. 11B, the call stack is CAB, and thestatistics kept for this node are 2:3:4:1. Note that call stack CAB isfirst produced at time 2 in FIG. 10A, and is exited at time 3. Callstack CAB is produced again at time 4, and is exited at time 7. Thus,the first statistic indicates that this particular call stack, CAB, isproduced twice in the trace. The second statistic indicates that callstack CAB exists for three units of time (at time 2, time 4, and time6). The third statistic indicates the cumulative amount of time spent incall stack CAB and those call stacks invoked from call stack CAB (i.e.,those call stacks having CAB as a prefix, in this case CABB). Thecumulative time in the example shown in FIG. 11B is four units of time.Finally, the recursion depth of call stack CAB is one, as none of thethree routines present in the call stack have been recursively entered.

Those skilled in the art will appreciate that the tree structuredepicted in FIG. 11B may be implemented in a variety of ways, and avariety of different types of statistics may be maintained at each node.In the described embodiment, each node in the tree contains data andpointers. The data include the name of the routine at that node, and thefour statistics discussed above. Of course, many other types ofstatistical information may be stored at each node. In the describedembodiment, the pointers for each node include a pointer to the node'sparent, a pointer to the first child of the node (i.e. the left-mostchild), a pointer to the next sibling of the node, and a pointer to thenext instance of a given routine in the tree. For example, in FIG. 11B,node 1154 would contain a parent pointer to node 1156, a first childpointer to node 1158, a next sibling pointer equal to NULL (note thatnode 1154 does not have a next sibling), and a next instance pointer tonode 1162. Those skilled in the art will appreciate that other pointersmay be stored to make subsequent analysis more efficient. In addition,other structural elements, such as tables for the properties of aroutine that are invariant across instances (e.g., the routine's name),may also be stored.

The type of performance information and statistics maintained at eachnode are not constrained to time-based performance statistics. Thepresent invention may be used to present many types of trace informationin a compact manner which supports performance queries. For example,rather than keeping statistics regarding time, tracing may be used totrack the number of Java bytecodes executed in each method (i.e.,routine) called. The tree structure of the present invention would thencontain statistics regarding bytecodes executed rather than time. Inparticular, the quantities recorded in the second and third categorieswould reflect the number of bytecodes executed rather than the amount oftime spent in each method.

Tracing may also be used to track memory allocation and deallocation.Every time a routine creates an object, a trace record could begenerated. The tree structure of the present invention would then beused to efficiently store and retrieve information regarding memoryallocation. Each node would represent the number of method calls, theamount of memory allocated within a method, the amount of memoryallocated by methods called by the method, and the number of methodsabove this instance (i.e., the measure of recursion). Those skilled inthe art will appreciate that the tree structure of the present inventionmay be used to represent a variety of performance data in a manner whichis very compact, and allows a wide variety of performance queries to beperformed.

The tree structure shown in FIG. 11B depicts one way in which data maybe pictorially presented to a user. The same data may also be presentedto a user in tabular form as shown in FIG. 12.

With reference now to FIG. 12, a call stack tree presented as a tablewill now be described. Note that FIG. 12 contains a routine, pt_pidtid,which is the main process/thread which calls routine C. Table 12includes columns of data for Level 1230, RL 1232, Calls 1234, Base 1236,Cum. 1238, and Indent 1240. Level 1230 is the tree level (counting fromthe root as level 0) of the node. RL 1232 is the recursion level. Calls1234 is the number of occurrences of this particular call stack, i.e.,the number of times this distinct call stack configuration occurs. Base1236 is the total observed time in the particular call stack, i.e., thetotal time that the stack had exactly these routines on the stack. Cum1238 is the total time in the particular call stack plus deeper levelsbelow it. Indent 1240 depicts the level of the tree in an indentedmanner. From this type of call stack configuration information, it ispossible to infer each unique call stack configuration, how many timesthe call stack configuration occurred, and how long it persisted on thestack. This type of information also provides the dynamic structure of aprogram, as it is possible to see which routine called which otherroutine. However, there is no notion of time-order in the call stacktree. It cannot be inferred that routines at a certain level were calledbefore or after other routines on the same level.

The pictorial view of the call stack tree, as illustrated in FIG. 11B,may be built dynamically or built statically using a trace text file orbinary file as input. FIG. 13 depicts a flow chart of a method forbuilding a call stack tree using a trace text file as input. In FIG. 13,the call stack tree is built to illustrate module entry and exit points.

With reference now to FIG. 13, it is first determined if there are moretrace records in the trace text file (step 1350). If so, several piecesof data are obtained from the trace record, including the time, whetherthe event is an enter or an exit, and the module name (step 1352). Next,the last time increment is attributed to the current node in the tree(step 1354). A check is made to determine if the trace record is anenter or an exit record (step 1356). If it is an exit record, the treeis traversed to the parent (using the parent pointer), and the currenttree node is set equal to the parent node (step 1358). If the tracerecord is an enter record, a check is made to determine if the module isalready a child node of the current tree node (step 1360). If not, a newnode is created for the module and it is attached to the tree below thecurrent tree node (step 1362). The tree is then traversed to themodule's node, and the current tree node is set equal to the module node(step 1364). The number of calls to the current tree node is thenincremented (step 1366). This process is repeated for each trace recordin the trace output file, until there are no more trace records to parse(step 1368).

With reference now to FIG. 14, a flow chart depicts a method forbuilding a call stack tree dynamically as tracing is taking place duringsystem execution. In FIG. 14, as an event is logged, it is added to thetree in real time. Preferably, a call stack tree is maintained for eachthread. The call stack tree reflects the call stacks recorded to date,and a current tree node field indicates the current location in aparticular tree. When an event occurs (step 1470), the thread ID isobtained (step 1471). The time, type of event (i.e., in this case,whether the event is a method entry or exit), the name of the module(i.e., method), location of the thread's call stack, and location of thethread's “current tree node” are then obtained (step 1472). The lasttime increment is attributed to the current tree node (step 1474). Acheck is made to determine if the trace event is an enter or an exitevent (step 1476). If it is an exit event, the tree is traversed to theparent (using the parent pointer), and the current tree node is setequal to the parent node (step 1478). At this point, the tree can bedynamically pruned in order to reduce the amount of memory dedicated toits maintenance (step 1479). Pruning is discussed in more detail below.If the trace event is an enter event, a check is made to determine ifthe module is already a child node of the current tree node (step 1480).If not, a new node is created for the module, and it is attached to thetree below the current tree node (step 1482). The tree is then traversedto the module's node, and the current tree node is set equal to themodule node (step 1484). The number of calls to the current tree node isthen incremented (step 1486). Control is then passed back to theexecuting module, and the dynamic tracing/reduction program waits forthe next event to occur (step 1488).

One of the advantages of using the dynamic tracing/reduction techniquedescribed in FIG. 14 is its enablement of long-term system tracecollection with a finite memory buffer. Very detailed performanceprofiles may be obtained without the expense of an “infinite” tracebuffer. Coupled with dynamic pruning, the method depicted in FIG. 14 cansupport a fixed-buffer-size trace mechanism.

The use of dynamic tracing and reduction (and dynamic pruning in somecases) is especially useful-in profiling the performance characteristicsof long running programs. In the case of long running programs, a finitetrace buffer can severely impact the amount of useful trace informationthat may be collected and analyzed. By using dynamic tracing andreduction (and perhaps dynamic pruning), an accurate and informativeperformance profile may be obtained for a long running program.

Many long-running applications reach a type of steady-state, where everypossible routine and call stack is present in the tree and updatingstatistics. Thus, trace data can be recorded and stored for suchapplications indefinitely within the constraints of a bounded memoryrequirement using dynamic pruning. Pruning has value in reducing thememory requirement for those situations in which the call stacks areactually unbounded. For example, unbounded call stacks are produced byapplications that load and run other applications.

Pruning can be performed in many ways, and a variety of pruning criteriais possible. For example, pruning decisions may be based on the amountof cumulative time attributed to a subtree. Note that pruning may bedisabled unless the amount of memory dedicated to maintaining the callstack exceeds some limit. As an exit event is encountered (such as step1478 in FIG. 14), the cumulative time associated with the current nodeis compared with the cumulative time associated with the parent node. Ifthe ratio of these two cumulative times does not exceed a pruningthreshold (e.g., 0.1), then the current node and all of its descendantsare removed from the tree. The algorithm to build the tree proceeds asbefore by traversing to the parent, and changing the current node to theparent.

Many variations of the above pruning mechanism are possible. Forexample, the pruning threshold can be raised or lowered to regulate thelevel of pruning from very aggressive to none. More global techniquesare also possible, including a periodic sweep of the entire call stacktree, removing all subtrees whose individual cumulative times are not asignificant fraction of their parent node's cumulative times.

Data reduction allows analysis programs to easily and quickly answermany questions regarding how computing time was spent within the tracedprogram. This information may be gathered by “walking the tree” andaccumulating the data stored at various nodes within the call stacktree, from which it can be determined the amount of time spent strictlywithin routine A, the total amount of time spent in routine A and in theroutines called by routine A either directly or indirectly, etc.

With reference now to the FIG. 15, a diagram of a structured profileobtained using the processes of the present invention is illustrated.Profile 1500 shows sample numbers in column 1502. Column 1504 shows thecall stack with an identification of the functions present within thecall stack at different sample times.

With reference now to FIG. 16, a diagram of a record generated using theprocesses of present invention is depicted. Each routine in record 1600is listed separately, along with information regarding the routine inFIG. 16. For example, calls column 1604 lists the number of times eachroutine has been called. BASE column 1606 contains the total time spentin the routine, while CUM column 1608 includes the cumulative time spentin the routine and all routines called by the routine. Name column 1612contains the name of the routine.

With reference now to FIG. 17, a diagram of another type of report thatmay be produced is depicted. The report depicted in FIG. 17 illustratesmuch of the same information found in FIG. 16, but in a slightlydifferent format. As with FIG. 16, diagram 1700 includes information oncalls, base time, and cumulative time.

FIG. 17 shows a sample-based trace output containing times spent withinvarious routines as measured in microseconds. FIG. 17 contains onestanza (delimited by horizontal lines) for each routine that appears inthe sample-based trace output. The stanza contains information about theroutine itself on the line labeled “Self”, about who called it on lineslabeled “Parent”, and about who the routine called on lines labeled“Child”. The stanzas are in order of cum time. The third stanza is aboutroutine A, as indicated by the line beginning with “Self.” The numberson the “Self” line of this stanza show that routine A was called threetimes in this trace, once by routine C and twice by routine B. In theprofile terminology, routines C and B are (immediate) parents of routineA. Routine A is a child of routines C and B. All the numbers on the“Parent” rows of the second stanza are breakdowns of routine A'scorresponding numbers. Three microseconds of the seven microsecond totalbase time spent in A was when it was called by routine C, and threemicroseconds when it was first called by routine B, and another onemicrosecond when it was called by routine B for a second time. Likewise,in this example, half of routine A's fourteen microsecond cum time wasspent on behalf of each parent.

Looking now at the second stanza, we see that routine C called routine Band routine A once each. All the numbers on “Child” rows are subsets ofnumbers from the child's profile. For example, of the three calls toroutine A in this trace, one was by routine C; of routine A's sevenmicrosecond total base time, three microseconds were while it was calleddirectly by routine C; of routine A's fourteen microsecond cum time,seven microseconds was on behalf of routine C. Notice that these samenumbers are the first row of the third stanza, where routine C is listedas one of routine A's parents.

The four relationships that are true of each stanza are summarized atthe top of FIG. 17. First, the sum of the numbers in the Calls columnfor parents equals the number of calls on the self row. Second, the sumof the numbers in the Base column for parents equals Self's base. Third,the sum of the numbers in the Cum column for parents equals Self's Cum.These first three invariants are true because these characteristics arethe definition of Parent; collectively they are supposed to account forall of Self's activities. Fourth, the Cum in the Child rows accounts forall of Self's Cum except for its own Base.

Program sampling contains information from the call stack and provides aprofile, reflecting the sampling of an entire call stack, not just theleaves. Furthermore, the sample-based profiling technique may also beapplied to other types of stacks. For example, with Java programs, alarge amount of time is spent in a routine called the “interpreter”. Ifonly the call stack was examined, the profile would not reveal muchuseful information. Since the interpreter also tracks information in itsown stack, e.g., a Java stack (with its own linkage conventions), theprocess can be used to walk up the Java stack to obtain the callingsequence from the perspective of the interpreted Java program.

With reference now to FIG. 18, a figure depicts a report generated froma trace file containing both event-based profiling information (methodentry/exits) and sample-based profiling information (stack unwinds).FIG. 18 is similar to FIG. 12, in which a call stack tree is presentedas a report, except that FIG. 18 contains embedded stack walkinginformation. Call stack tree 1800 contains two stack unwinds generatedwithin the time period represented by the total of 342 ticks. Stackunwind identifier 1802 denotes the beginning of stack unwind information1806, with the names of routines that are indented to the rightcontaining the stack information that the stack walking process was ableto discern. Stack unwind identifier 1804 denotes the beginning of stackunwind information 1808. In this example, “J:” identifies an interpretedJava method and “F:” identifies a native function, such as a nativefunction within JavaOS. A call from a Java method to a native method isvia “ExecuteJava.” Hence, at the point at which the stack walkingprocess reaches a stack frame for an “ExecuteJava,” it cannot proceedany further up the stack as the stack frames are discontinued. Theprocess for creating a tree containing both event-based nodes andsample-based nodes is described in more detail further below. In thiscase, identifiers 1802 and 1804 also denote the major code associatedwith the stack unwind.

With reference now to FIGS. 19A-19B, tables depict major codes and minorcodes that may be employed to instrument software modules for profiling.In order to facilitate the merging of event-based profiling informationand sample-based profiling information, a set of codes may be used toturn on and off various types of profiling functions.

For example, as shown in FIGS. 19A-19B, the minor code for a stackunwind is designated as 0x7fffffff, which may be used for two differentpurposes. The first purpose, denoted with a major code of 0x40, is for astack unwind during a timer interrupt. When this information is outputinto a trace file, the stack information that appears within the filewill have been coded so that the stack information is analyzed assample-based profiling information. The second purpose, denoted with amajor code of 0x41, is for a stack unwind in an instrumented routine.This stack information could then be post-processed as event-basedprofiling information.

Other examples in the table show a profile or major code purpose oftracing jitted methods with a major code value of 0x50. Tracing ofjitted methods may be distinguished based on the minor code thatindicates method invocation or method exit. In contrast, a major code of0x30 indicates a profiling purpose of instrumenting interpreted methods,while the minor code again indicates, with the same values, methodinvocation or method exit.

Referring back to FIG. 18, the connection can be made between the use ofmajor and minor codes, the instrumentation of code, and thepost-processing of profile information. In the generated report shown inFIG. 18, the stack unwind identifiers can be seen to be equal to 0x40,which, according to the table in FIGS. 19A-19B, is a stack unwindgenerated in response to a timer interrupt. This type of stack unwindmay have occurred in response to a regular interrupt that was created inorder to generate a profile of executing software.

As noted in the last column of the table in FIGS. 19A-19B, by using autility that places a hook into a software module to be profiled, astack unwind may be instrumented into a routine. If so, the output forthis type of stack unwind will be designated with a major code of 0x41.

As noted previously, profiling the execution of a program may introducedisruptive effects and costs associated with the profiling mechanismitself. One of the costs is the temporal overhead associated withgenerating and recording trace records that contain information obtainedduring profiling. The present invention is directed to a process foranalyzing and compensating for the temporal overhead associated withgenerating and recording trace information.

With reference now to FIG. 20A, a flowchart depicts a summary of themanner in which elapsed time is attributed to various routines in anexecution flow. FIG. 11B depicts a call stack tree representation thatshows the number of times that a routine has been called, the base timeassociated with the routine, the cumulative time associated with theroutine, and its relative position within a call stack sequence orexecution flow. The process for constructing this type of call stacktree representation is depicted within the flowchart shown in FIG. 13.FIG. 20A is merely a summary of processes previously described fordetermining how to interpret timestamps generated during a trace andattribute the captured times among routines within execution flows.

Although the processes are described in terms of event times, it shouldbe noted that the processes are generally applicable to any timestampthat appears within trace data related to the execution of profiledroutines.

The present invention generally describes the manipulation of eventtimes and the computation of event time differences. An event time isthe system-relative point in time at which an event related to theexecution of a program is said to have occurred. These events may bemethod entry events, method exit events, class loading events, classunloading events, etc. The source of a particular time value is usuallya timestamp value returned by invoking a system function that determinesthe system time as kept by the operating system, the system hardware, orsome combination of the hardware and operating system. These timestampsmay be used in real-time or may be recorded within event records in amemory buffer or an output file and then post-processed.

The process begins with the retrieval of the event time for a previousevent (step 2002). If the trace information associated with events isbeing processed during a post-processing phase, the previous event timemay have been previously stored as a variable at some point in time,e.g., after processing a record containing timestamps of the previousevent, which may have been previously retrieved from a trace file ortrace buffer. If the trace information is being processed in real-time,then the previous event time may have been temporarily stored inanticipation of subsequent processing of the next event. The previousevent time may be initialized to zero when the profiler is firstinvoked.

The next trace record in the trace file is then retrieved and parsed(step 2004), and the current event time is obtained from the timestampinformation in the current trace record (step 2006). The last timeincrement, or delta event time, is computed as the difference betweenthe current event time and the previous event time (step 2008). Thedelta event time is then attributed to the current routine (step 2010)in some manner, such as adding the delta event time to the base timestored in the node associated with the routine within a call stack tree.The process is then complete with respect to computing a delta eventtime and attributing it to a routine.

With reference now to FIG. 20B, a timeline depicts demarcated timepoints with which the process in FIG. 20A is concerned. The previousevent time is the time point at which a first event (previous event) hasoccurred, such as an entry event or an exit event. The current eventtime is the time point at which a second event (current event)subsequent to the first event has been recorded as occurring, such asthe next entry event or exit event. The difference between the previousevent time and the current event time is shown as the delta event time,also described as the last time increment. Again, it should be notedthat the “event” time may be generically replaced by any relevant“profile” time or “trace” time that denotes a point in time at whichsome action of interest to the profiler has occurred.

With reference now to FIG. 21, a trace record format is depicted thatcontains information from which compensation information may be obtainedfor compensating for trace record generation overhead. Trace record 2100contains various fields, such as record length, major code, minor code,trace time, and other trace record data. Trace record 2100 may begenerated by a profiler routine at the application level or at thesystem level, such as by a routine within a device driver or a routinewithin the kernel or a kernel extension.

It should be noted that the measured times within the processes of thepresent invention can be any monotonically increasing measure, such ascycles, cache misses, microseconds, milliseconds, etc.

The delta event time is, ideally, a temporal measure between therecording of two events. However, because the generation of a tracerecord requires a certain amount of time, the entire delta event time isnot consumed by the execution of the current routine—some of the time isconsumed by the execution of the profiler. Although the generation of asingle trace record may consist of a relatively short amount of time,the profiler may process trace events and could also generate thousandsof trace records from the trace events. In a typical case, the traceevents and/or records are generated via an instrumented application oroperating system. The total amount of time spent generating the tracerecords may then grow to a significant percentage of the overallexecution time. If this profiling overhead time is not determined andthe base time of the profiled routines are not compensated for thisoverhead time, then the profile of the execution of the program maycontain significant distortion as to the time spent within variousroutines.

With reference now to FIG. 22, a flowchart depicts a process forprofiling the execution of a program with the determination ofcalibration values to be used to compensate for the instrumentationoverhead introduced by the profiling processing. The process begins witha calibration phase (step 2202) followed by an initialization phase(step 2204). Once the initialization is complete, the program isprofiled during the profiling phase (step 2206). After the executionflows are recorded within a trace file, the trace file is post-processedduring a post-processing phase (step 2208) to generate profile reports,etc.

The process depicted in FIG. 22 is similar to the process depicted inFIG. 5 except that the process depicted in FIG. 22 includes acalibration phase. Since the time required to generate a trace record isa function of the amount of data outputted to the trace record, anattempt is made to calibrate the trace overhead by determining theamount of time used during the output processing for the trace records.Other variables may be significant to accurately calibrating the traceoutput. Therefore, the calibration phase may be completed at varioustimes and locations. A calibration run may be performed on eachindividual machine as the performance on each computer platform maydiffer depending upon the hardware configuration. If other applicationsare executing on a computer while another application is to be profiled,then these other applications may perform operations that affect theoutput of trace records during the profiling phase. In this instance, acalibration phase may be performed immediately before profiling anapplication, such as is depicted in FIG. 22. This may provide anaccurate and up-to-date estimate of the amount of time associated withwriting a trace record while the computer has an execution loadconsisting of several applications.

With reference now to FIG. 23, a flowchart depicts a process fordetermining an output overhead calibration value by comparing acalibration run with trace output enabled with a calibration run withtrace output disabled. The process begins by obtaining the start timefor a first calibration run from the system (step 2302). An applicationprogram is then profiled with trace output enabled (step 2304). Theapplication program may be a benchmark program or may be an actualinstrumented program for which one is attempting to determine a detailedcalibration value for this particular application program. The endingtime of the first calibration run is then obtained from the system (step2306).

The starting time of a second calibration run is then obtained from thesystem (step 2308), and the application program is profiled with traceoutput disabled (step 2310). The application program that is profiledduring the second calibration run should be identical to the applicationprofile that was profiled during the first calibration run. The endingtime of the second calibration run is then obtained from the system(step 2312). The process then computes the difference in execution timebetween the first calibration run and the second calibration run andalso obtains the number of trace records in the trace file generatedduring the first calibration run (step 2314). The difference in timebetween the execution of the first and second calibration runs isassumed to be caused by the generation of trace output overhead duringthe first calibration run. Hence, an output overhead calibration valueis computed as the average time for writing a trace record using thetime difference between the two calibration runs (step 2316). Thecomputed output overhead calibration value is then stored (step 2318).The process is then complete with respect to obtaining an outputoverhead calibration value using this particular method.

This output overhead calibration value may be stored in a variety ofmanners. For example, it may be stored as an environment value that isaccessible by multiple profiling programs through an operating systemcall on a computer system. Alternatively, the output overheadcalibration value may be stored as a property within a Java propertiesfile that is accessible to a profiling program within a Java runtimeenvironment.

Alternatively, the output overhead calibration value may be stored inassociation with the application for which the calibration run has beenexecuted. The calibration run may be executed immediately beforeprofiling the application. This may be useful if the application isgenerally executed in the same runtime environment with the sameprocessing load on the computer platform. By associating the calibrationvalue with the application, the calibration value may represent a moreaccurate determination of the overhead associated with the processingloads that are encountered during the execution of the application.

The calibration runs may be performed more than once to obtain anaverage value of several runs, which may be more accurate than theaverage trace overhead compensation value determined from a single run.

With reference now to FIG. 24A, a flowchart depicts a process forcompensating for trace overhead using a trace overhead compensationvalue. FIG. 24A is similar to FIG. 20A except that the process depictedin FIG. 24A includes an approach for compensating for the trace overheadassociated with generating a trace record during the profiling process.

The process begins by retrieving the previous event time that was savedafter processing the previous trace record (step 2402). The next tracerecord is retrieved from the trace file or trace buffer (step 2404) andparsed to get the current event time (step 2406). The delta event timeis computed as the difference between the current event time and theprevious event time (step 2408). A trace overhead compensation value isretrieved (step 2410), and the trace overhead compensation value issubtracted from the delta event time (step 2412). If the adjusted deltaevent time is less than zero, then the adjusted delta event time is setto zero (step 2413) in order to compensate for a situation in which thetrace overhead compensation value is greater than some of the deltaevent times. The adjusted delta event time is then attributed to thecurrent routine (step 2414). The process is then complete with respectto computing an adjusted delta event time and attributing it to theproper routine. The trace compensation value may be equal to apreviously determined trace calibration value or, alternatively, thetrace compensation value may be proportional to a previously determinedtrace calibration value.

With reference now to FIG. 24B, a timeline depicts the time points withwhich the process within FIG. 24A is concerned. The timeline depicted inFIG. 24B is similar to the timeline depicted in FIG. 20B except that thetimeline in FIG. 24B also includes an indication of the output overheadtime for which the present invention attempts to compensate.

As described above, the previous event time demarcates the recording ofa previous event, and the current event time demarcates the recording ofa current event. The time between the recording of the two eventsrepresents a delta event time. As shown in FIG. 24B, the delta eventtime consists of two times: a trace overhead time and an adjusted deltaevent time. The trace overhead time is determined and stored as a traceoverhead calibration value or trace overhead compensation value. Byassuming that every trace record of a certain size requires the sameamount of time to output to the trace file or trace buffer, this traceoverhead calibration value may be subtracted from the delta event timeto obtain an adjusted delta event time.

It should be noted that one trace overhead compensation value may beapplied against a set of trace records for one purpose and anotheroverhead compensation value may be applied against the same tracerecords for another purpose. For example, FIG. 24 shows a process forcompensating for the overhead associated with the generation and/oroutput of trace records. Event times are derived from the trace records,and a compensation value is applied against those event times in anattempt to gain more accurate representations of the execution times ofthe routines being profiled. However, a profiler may also compensate forthe overhead associated with other aspects of the profiling process sothat the execution times attributed to the routines within a programbeing profiled have been adjusted for more than one effect. For example,another effect could include the overhead time required to invoke aprofiling process when an event occurs, which is separate and distinctfrom the time required for the profiling process to generate or output atrace record.

FIG. 24 depicts a process for adjusting delta times on-the-fly as thedelta event times are determined. Alternatively, the delta event timesmay be attributed to routines and stored/added to base times withinnodes representing the routines in a call stack tree structure, and thebase times are later adjusted using one or more trace overheadcompensation values. Preferably, the trace overhead compensation valuesare employed in the manner shown in FIGS. 25A-25B.

With reference now to FIG. 25A, a flowchart depicts a second process forcompensating for trace overhead using a trace overhead compensationvalue. The process begins by retrieving a previous event time that wassaved after processing the previous trace record (step 2502). The nexttrace record is retrieved from the trace file (step 2504) and parsed toget the timestamp of a current event, i.e. an event represented by thetrace record that is currently being processed (step 2506). The deltaevent time is computed as the difference between the trace time of thecurrent event record and the previous event time (step 2508).

If the computed delta event time is a negative value, then the deltaevent time is set to zero (step 2510). This is a required step toprevent a negative time interval being computed and attributed to aroutine, which may occur if the timestamp associated with a trace recordis adjusted to compensate for various overhead considerations, asdescribed below.

The computed delta event time is then attributed to the current routine(step 2512). The previous event time is then adjusted by adding a traceoverhead compensation value equal to the average amount of time requiredto generate a trace record (step 2514). The previous event time is thensaved (step 2516). The process is then complete with respect tocomputing a delta event time and attributing it to the proper routine.

With reference now to FIG. 25B, a timeline depicts the time points withwhich the process within FIG. 25A is concerned. The timeline depicted inFIG. 25B is similar to the timeline depicted in FIG. 20B except that thetimeline in FIG. 25B also includes an indication of the trace recordgeneration overhead time for which the present invention attempts tocompensate.

As described above, time point 2552 demarcates the previous event time,i.e. the time that was saved as the previous event. Time point 2550demarcates the timestamp that was stored within the trace recordassociated with the previous event. The time between the two pointsrepresents the average overhead required to generate a single tracerecord, and trace record generation overhead time period 2554 has beenused to compute a saved time for the previous event.

Time period 2556 represents the delta event time, i.e. the amount oftime attributable to the execution of the current routine, i.e. theroutine associated with the trace record that is currently beingprocessed. Time point 2558 is the timestamp stored within the tracerecord of the current event, i.e. the event currently being processed.

The advantages of the present invention should be apparent in view ofthe detailed description of the invention provided above. Trace recordsof some form are necessary as external evidence of the internaloperations of a program that is being profiled. However, the generationof the trace records introduces some distortion in the execution flowsof the profiled program. With the present invention, one can attempt todetermine the amount of trace record generation overhead caused bycreating the trace records necessary for providing profile information.The base times of the routines within the execution flows of theprofiled program may then be computed without this trace recordgeneration overhead.

It is important to note that while the present invention has beendescribed in the context of a fully functioning data processing system,those of ordinary skill in the art will appreciate that the processes ofthe present invention are capable of being distributed in the form of acomputer readable medium of instructions and a variety of forms and thatthe present invention applies equally regardless of the particular typeof signal bearing media actually used to carry out the distribution.Examples of computer readable media include recordable-type media such afloppy disc, a hard disk drive, a RAM, and CD-ROMs and transmission-typemedia such as digital and analog communications links.

The description of the present invention has been presented for purposesof illustration and description, but is not intended to be exhaustive orlimited to the invention in the form disclosed. Many modifications andvariations will be apparent to those of ordinary skill in the art. Theembodiment was chosen and described in order to best explain theprinciples of the invention, the practical application, and to enableothers of ordinary skill in the art to understand the invention forvarious embodiments with various modifications as are suited to theparticular use contemplated.

What is claimed is:
 1. A method for determining temporal overhead ingenerating trace records in a data processing system, the methodcomprising the computer-implemented steps of: profiling a program withenablement of trace record generation during a first time period;profiling a program with disablement of trace record generation during asecond time period; obtaining a number of trace records generated duringthe first time period; calculating a trace overhead time value as adifference in length between the first time period and the second timeperiod; computing a trace overhead calibration value as an average timefor writing the number of trace records output during the trace overheadtime value; and storing the output overhead calibration value forsubsequent use in a profiling-related process in the data processingsystem.
 2. The method of claim 1, wherein the step of profiling aprogram with enablement of trace record generation during the first timeperiod further comprises: retrieving a first start time; executing theprogram during which trace records are generated; retrieving a first endtime; and computing the first time period as a difference between thefirst start time and the first end time.
 3. The method of claim 1,wherein the step of profiling a program with disablement of trace recordgeneration during the second time period further comprises: retrieving asecond start time; executing the program during which trace records arenot generated; retrieving a second end time; and computing the secondtime period as a difference between the first start time and the firstend time.
 4. A method for processing trace data output in a dataprocessing system, the method comprising the computer-implemented stepsof: reading a first timestamp from a first trace record and a secondtimestamp from a second trace record; computing the difference betweenthe two timestamps to obtain a delta time; retrieving a trace overheadcompensation value; and adjusting the delta time by subtracting thetrace overhead compensation value, wherein the trace overhead isdetermined by comparing a calibration run preceding a trace process. 5.The method of claim 4, wherein a calibration run compares an executiontime of a program with profiling and an execution time of the programwithout profiling.
 6. The method of claim 5 further comprising:computing an average amount of time to generate a trace record duringthe calibration run.
 7. The method of claim 4 wherein the trace overheadcompensation value is directly proportional to a time required togenerate a trace record.
 8. The method of claim 4 wherein the firsttrace record and the second trace record are output to a trace fileduring execution of a profiling process in the data processing system.9. The method of claim 4 further comprising: attributing the adjusteddelta time to a routine whose execution caused the first trace record orthe second trace record to be output.
 10. The method of claim 4 furthercomprising: adjusting the delta time using a plurality of trace overheadcompensation values.
 11. The method of claim 4 further comprising:setting the adjusted delta time to zero if the adjusted delta time isnegative.
 12. A data processing system for determining temporal overheadin generating trace records in the data processing system, the dataprocessing system comprising: first profiling means for profiling aprogram with enablement of trace record generation during a first timeperiod; second profiling means for profiling a program with disablementof trace record generation during a second time period; obtaining meansfor obtaining a number of trace records generated during the first timeperiod; calculating means for calculating a trace overhead time value asa difference in length between the first time period and the second timeperiod; first computing means for computing a trace overhead calibrationvalue as an average time for writing the number of trace records outputduring the trace overhead time value; and storing means for storing theoutput overhead calibration value for subsequent use in aprofiling-related process in the data processing system.
 13. The dataprocessing system of claim 12, wherein the profiling means for profilinga program with enablement of trace record generation during the firsttime period further comprises: first retrieving means for retrieving afirst start time; first executing means for executing the program duringwhich trace records are generated; second retrieving means forretrieving a first end time; and second computing means for computingthe first time period as a difference between the first start time andthe first end time.
 14. The data processing system of claim 12, whereinthe profiling means for profiling a program with disablement of tracerecord generation during the second time period further comprises: thirdretrieving means for retrieving a second start time; second executingmeans for executing the program during which trace records are notgenerated; third retrieving means for retrieving a second end time; andthird computing means for computing the second time period as adifference between the first start time and the first end time.
 15. Adata processing system for processing trace data output in the dataprocessing system, the data processing system comprising: reading meansfor reading a first timestamp from a first trace record and a secondtimestamp from a second trace record; first computing means forcomputing the difference between the two timestamps to obtain a deltatime; retrieving means for retrieving a trace overhead compensationvalue; and first adjusting means for adjusting the delta time bysubtracting the trace overhead compensation value, wherein the traceoverhead is determined by comparing a calibration run preceding a traceprocess.
 16. The data processing system of claim 15, wherein acalibration run compares an execution time of a program with profilingand an execution time of the program without profiling.
 17. The dataprocessing system of claim 15 further comprising: second computing meansfor computing an average amount of time to generate a trace recordduring the calibration run.
 18. The data processing system of claim 15wherein the trace overhead compensation value is directly proportionalto a time required to generate a trace record.
 19. The data processingsystem of claim 15 wherein the first trace record and the second tracerecord are output to a trace file during execution of a profilingprocess in the data processing system.
 20. The data processing system ofclaim 15 further comprising: attributing means for attributing theadjusted delta time to a performance statistic for a routine whoseexecution caused the first trace record or the second trace record to beoutput.
 21. The data processing system of claim 15 further comprising:second adjusting means for adjusting the delta time using a plurality oftrace overhead compensation values.
 22. The data processing system ofclaim 15 further comprising: setting means for setting the adjusteddelta time to zero if the adjusted delta time is negative.
 23. Acomputer program product in a computer-readable medium for use in a dataprocessing system for determining temporal overhead in generating tracerecords, the computer program product comprising: first instructions forprofiling a program with enablement of trace record generation during afirst time period; second instructions for profiling a program withdisablement of trace record generation during a second time period;third instructions for obtaining a number of trace records generatedduring the first time period; fourth instructions for calculating atrace overhead time value as a difference in length between the firsttime period and the second time period; fifth instructions for computinga trace overhead calibration value as an average time for writing thenumber of trace records output during the trace overhead time value; andsixth instructions for storing the output overhead calibration value forsubsequent use in a profiling-related process in the data processingsystem.
 24. The computer program product of claim 23, wherein firstinstructions for profiling a program with enablement of trace recordgeneration during the first time period further comprise: instructionsfor retrieving a first start time; instructions for executing theprogram during which trace records are generated; instructions forretrieving a first end time; and instructions for computing the firsttime period as a difference between the first start time and the firstend time.
 25. The computer program product of claim 23, wherein thesecond instructions for profiling a program with disablement of tracerecord generation during the second time period further comprise:instructions for retrieving a second start time; instructions forexecuting the program during which trace records are not generated;instructions for retrieving a second end time; and instructions forcomputing the second time period as a difference between the first starttime and the first end time.
 26. A computer program product in acomputer-readable medium for use in a data processing system forprocessing trace data output, the computer program product comprising:first instructions for reading a first timestamp from a first tracerecord and a second timestamp from a second trace record; secondinstructions for computing the difference between the two timestamps toobtain a delta time; third instructions for retrieving a trace overheadcompensation value; and fourth instructions for adjusting the delta timeby subtracting the trace overhead compensation value, wherein the traceoverhead is determined by comparing a calibration run preceding a traceprocess.
 27. The computer program product of claim 26 wherein acalibration run compares an execution time of a program with profilingand an execution time of the program without profiling.
 28. The computerprogram product of claim 27 further comprising; instructions forcomputing an average amount of time to generate a trace record duringthe calibration run.
 29. The computer program product of claim 26wherein the trace overhead compensation value is directly proportionalto a time required to generate a trace record.
 30. The computer programproduct of claim 26 wherein the first trace record and the second tracerecord are output to a trace file during execution of a profilingprocess in the data processing system.
 31. The computer program productof claim 26 further comprising: instructions for attributing theadjusted delta time to a performance statistic for a routine whoseexecution caused the first trace record or the second trace record to beoutput.
 32. The computer program product of claim 26 further comprising:instructions for adjusting the delta time using a plurality of traceoverhead compensation values.
 33. The computer program product of claim26 further comprising: instructions for setting the adjusted delta timeto zero if the adjusted delta time is negative.